In the early 1990s,Howard Dresner, then an analyst at the Gartner Group, coined the term business intelligence due to the growing need for applications designedto support decision making based on data collected. Nowadays, business leadersand top management have access to more data than ever before; however data byitself doesn’t generate insights. Business Intelligence (BI) Tools have becomethe go-to resource for helping companies harness the power of big data andanalytics and make smarter, data-driven decisions.During the various years, there have been variousdefinitions of BI according to its form, usage and the industry it is appliedto. Many of them are focused only on the software used for businessintelligence and neglect to include the primary goal of business intelligence.
While the term is often heard in relation to software vendors, there’s more toBI than just software tools. LiteratureReviewBusiness intelligencebecame a popular term in the business and Information Technology (IT)communities only in the 1990s. Business intelligence (BI) refers to a managerialphilosophy and a tool used to help organizations manage and refine businessinformation with the objective of making more effective business decisions(Ghoshal and Kim, 1986; Gilad and Gilad, 1986). Dresner (1988) defined businessintelligence as the “concepts and methods to improve business decision makingby using fact-based support systems.” The term BI can either be used to referto the relevant information and knowledge describing the business environment,the organization itself, and its situation in relation to its markets,customers, competitors, and economic issues or to an organized and systematicprocess by which organizations acquire, analyze, and disseminate informationfrom both internal and external information sources significant for theirbusiness activities and for decision making (Lönnqvist and Pirttimäki, 2006).In European literature, theterm BI is considered a broad umbrella concept for competitive intelligence (CI)and other intelligence-related terms, such as market intelligence, customerintelligence, competitor intelligence, strategic intelligence, and technicalintelligence. Indeed the term has been defined front several perspectives(Casado, 2004), however they all focus on a shared purpose, analyzing data andinformation. As Gilad and Gilad (1986) have stated, organizations have collectedinformation about their competitors since the dawn of capitalism.
The real revolutionis in the efforts to institutionalize intelligence activities.BI presents businessinformation in a timely and easily consumed way and provides the ability toreason and understand the meaning behind business information through, forexample, discovery, analysis, and ad-hoc querying (Azoff and Charlesworth,2004). Today, business intelligence is defined by Evelson and Nicolson (2008)at the Forrester as “a set of methodologies, processes, architectures, andtechnologies that transform raw data into meaningful and useful informationused to enable more effective strategic, tactical, and operational insights anddecision-making.” Business Intelligence today is never a new technology insteadof an integrated solution for companies, within which the business requirementis definitely the key factor that drives technology innovation (Ranjan, 2009).
Ranjan (2009) stated that the major challenge of a BI application to achievereal business impact is to identify and creatively address key business issues.After discussing the manydefinitions of BI, the question of why do companies use it naturally arises. Theprimary goal is to stay ahead of the competition and make the right decision atthe right time. Those decisions can be made around pretty much any aspect ofrunning a business, such as figuring out how to increase the effectiveness ofmarketing campaigns, deciding whether and when to enter new markets, and improvingproducts and services to better meet customers’ needs. One of the key aspectsof business intelligence is that it’s designed to put information in the handsof business users. Organizations are required to make decisions at anincreasingly faster pace, so today’s business intelligence tools help decisionmakers access the information they need without having to first go through theIT department or specifically designated data scientists.Componentsor Tools of BIBI includes several softwarefor Extraction, Transformation and Loading (ETL), data warehousing, databasequery and reporting, (Berson et.
al, 2002; Curt Hall, 1999)multidimensional/on-line analytical processing (OLAP) data analysis, datamining and visualization. Figure 1: Business Intelligence DiagramDataand Data Sources Business intelligence all starts with the data. As mentionedin the introduction, businesses have access to more data than ever. Datasources can be operational databases, historical data, external data (frommarket research companies or from the Internet), or information from thealready existing data warehouse environment. The data sources can be relationaldatabases or any other data structure that supports the line of businessapplications. They also can reside on many different platforms and can containstructured information, such as tables or spreadsheets, or unstructuredinformation, such as plaintext files or pictures and other multimediainformation.Extract,Transform, Load (ETL) A key part of BI is the tools and processes used toprepare data for analysis. When data is created by different applications, it’snot likely all in the same format, and data from one application can’tnecessarily be looked at in relation to data from another.
In addition, ifbusiness intelligence is relied on to make critical decisions, businesses mustmake sure the data they are using is accurate. The process of getting dataready for analysis is known as Extract, Transform, and Load (ETL). The data isextracted from internal and external sources, transformed into a common format,and loaded into a data warehouse.
This process also typically includes dataintegrity checks to make sure the data being used is accurate and consistent.DataWarehouse and Data MartsThe data warehouse is thesignificant component of business intelligence. It is subject oriented,integrated. The ETL process ends with data being loaded into the warehouse,because when the data is contained within the separate sources, it’s not muchuse for intelligence. A data warehouse is a repository containing informationfrom all the business’s applications and systems, as well as external sources,so it can be analyzed together. A data mart as described by (Inmon, 1999) is acollection of subject areas organized for decision support based on the needsof a given department.
Similar to data warehouses,data marts contain operational data that helps business experts to strategizebased on analyses of past trends and experiences. The key difference is thatthe creation of a data mart is predicated on a specific, predefined need for acertain grouping and configuration of select data. There can be multiple datamarts inside an enterprise. A data mart can support a particular businessfunction, business process or business unit.OLAP(On-line analytical processing)It refers to the way inwhich business users can slice and dice their way through data usingsophisticated tools that allow for the navigation of dimensions such as time orhierarchies. Online Analytical Processing or OLAP provides multidimensional,summarized views of business data and is used for reporting, analysis, modelingand planning for optimizing the business.
OLAP techniques and tools can be usedto work with data warehouses or data marts designed for sophisticatedenterprise intelligence systems.AdvancedAnalyticsIt is referred to as datamining, forecasting or predictive analytics, this takes advantage ofstatistical analysis techniques to predict or provide certainty measures onfacts.CorporatePerformance Management (Portals, Scorecards, and Dashboards)This general categoryusually provides a container for several pieces to plug into so that theaggregate tells a story.
Realtime BIIt allows for the real timedistribution of metrics through email, messaging systems and / or interactivedisplays.DiscussionOverall, Business Intelligenceprovides benefits to companies utilizing it. Initially, BI reduces ITinfrastructure costs by eliminating redundant data extraction processes andduplicate data housed in independent data marts across the enterprise. For example,3M justified its multimillion- dollar data warehouse platform based on thesavings from data mart consolidation (Watson, Wixom, and Goodhue, 2004, pp.202-216). Moreover, it can eliminate a lot of the guesswork within anorganization, enhance communication among departments while coordinatingactivities, and enable companies to respond quickly to changes in financialconditions, customer preferences, and supply chain operations. Figure 2: Spectrum of BIbenefits.
As business users mature to performing analysis and prediction, thelevel of benefits become more global in scope and difficult to quantify.Over time, organizationsevolve to questions like “Why has this happened?” and even “What will happen?”As business users mature to performing analysis and prediction, the level ofbenefits become more global in scope and difficult to quantify (Watson and Wixom,2007). Information is often regarded as the second most important resource acompany has (a company’s most valuable assets are its people). So when acompany can make decisions based on timely and accurate information, thecompany can improve its performance. However, there are also a fewissues regarding Business Intelligence. Firstly,Most BI benefits are intangible before the fact. An empirical study for 50Finnish companies found most companies do not consider cost or time savings asprimary benefit when investing in BI systems (Hannula and Pirttimaki, 2003).The hope is that a good BI system will lead to a return at some time in thefuture.
Secondly, experts view BIin different ways. Ranjan (2009, pg 62-63) is of the opinion that to datamining experts BI is set of advanced decision support systems with data miningtechniques and applications of algorithms, while to statisticians BI is viewedas a forecasting and multidimensional analysis based tool. Data warehousingexperts view BI as supplementary systems and is very new to them.
These expertstreat BI as technology platform for decision support application.Third, very feworganizations have a full-fledged enterprise data warehouse. The main key tosuccessful BI system is consolidating data from the many different enterpriseoperational systems into an enterprise data warehouse. Berson (2002) emphasizes that in view of emerginghighly dynamic business environment, only the most competitive enterprises willachieve sustained market success. The organizations will distinguish themselvesby the capability to leverage information about their market place, customers,and operations to capitalize on the business opportunities.Conclusionsand Future StudyThe business intelligence(BI) has evolved over the past decade to rely increasingly on real time data. Enterprisestoday demand quick results and it is essential that not only is the businessanalysis done, but also actions in response to analysis of results andinstantaneously parameters’ changes of business processes.
The paper exploredthe concepts of BI, its components, benefits and issues of BI. It is important toexamine the impact BI has on each individual company and on the economy as a whole.The possible future of Business Intelligence lies in cloud computing.
Security,data protection, lack of control, and several other barriers prevent widespreadadoption of the BI; however cloud computing promises significant benefits, whichneed to be maturely and reasonably assessed.